ArticleIntensive care medicine experimental2025
Can patient-derived in vitro models improve clinical translation in critical care research when used before animal studies?
Article in Intensive care medicine experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Dual-Functional Self-Assembled Nanoparticles for Synergistic Photodynamic Therapy and Antimetastatic Treatment of Colorectal Cancer.Pharmaceutics · 2026Article
- Deranged mitochondrial metabolism in critical illness: how to fill the gap between preclinical knowledge and clinical application.Intensive care medicine · 2026Article
- Experimental models of cerebral small vessel disease: Physiological constraints, translational challenges and future directions.The Journal of physiology · 2026Review
- Targeting the heart-immune axis after myocardial infarction: from inflammation to immunomodulation.Frontiers in cardiovascular medicine · 2026Review
- Advances in Intranasal Delivery of Exosomes for Central Nervous System Disorders.Molecular neurobiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundTranslational failure remains a major barrier in critical illness research, with preclinical findings from animal models often failing to replicate in human trials. HYPOTHESIS: we hypothesize that the integration of advanced in vitro models derived from human cells-particularly those from ICU patients-prior to animal studies will enhance clinical translation in critical care research. MAIN TEXT: These emerging human-relevant platforms-such as organ-on-chip microfluidic systems-recapitulate key aspects of human physiology and pathology that animal models often cannot, thereby avoiding interspecies differences, capturing patient-specific variability, and enabling the study of disease phenotypes and endotypes. We propose that advanced in vitro models should be used first to gain mechanistic insights and assess efficacy in a human-relevant setting, while subsequent animal studies would then serve to evaluate systemic effects and safety before translation to patients. By leveraging such complementary strengths, an integrated in vitro-in vivo pipeline could better bridge the bench-to-bedside gap. This approach aligns with 3Rs principles by refining and reducing animal use (screening therapeutics in human models to focus subsequent animal experiments), and potentially replacing certain animal tests pending rigorous validation and regulatory acceptance. Implementation will require regulatory support, as well as training and funding to overcome technical barriers. This hypothesis is testable through analyses of past translational failures to determine whether human in vitro models could have predicted outcomes, and through prospective studies comparing drug development pipelines with and without an in vitro prescreening step to assess improvements in clinical success rates.
conclusionsBy harnessing the strengths of both model systems, this two-step strategy could help bridge the translational gap in critical care, improve therapeutic development, and accelerate precision medicine in sepsis and other critical illnesses.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.